Balasubramaniam Natarajan | DeepAI

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Foundational Models for Fault Diagnosis of Electrical Motors

A majority of recent advancements related to the fault diagnosis of elec...

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A General Framework for Uncertainty Quantification via Neural SDE-RNN

Uncertainty quantification is a critical yet unsolved challenge for deep...

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∙ 06/16/2022

Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications

Deep reinforcement learning (DRL) has empowered a variety of artificial ...

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∙ 05/30/2022

GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization

Influence maximization (IM) is a combinatorial problem of identifying a ...

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∙ 05/20/2022

A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks

Graph Neural Networks (GNN) provide a powerful framework that elegantly ...

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∙ 10/07/2021

Addressing practical challenges in Active Learning via a hybrid query strategy

Active Learning (AL) is a powerful tool to address modern machine learni...

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∙ 12/26/2020

Bayesian Inductive Learner for Graph Resiliency under uncertainty

In the quest to improve efficiency, interdependence and complexity are b...

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Graph neural network based approximation of Node Resiliency in complex networks

The emphasis on optimal operations and efficiency has led to increased c...

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∙ 03/10/2020

Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks

Emerging wireless services with extremely high data rate requirements, s...

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∙ 09/09/2019

Analyzing the Trade-offs in Using Millimeter Wave Directional Links for High Data Rate Tactile Internet Applications

Ultra-low latency and high reliability communications are the two defini...

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